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Informatics
Special Issues
Applications of Complex Networks: Advances and Challenges
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Applications of Complex Networks: Advances and Challenges

A special issue of Informatics (ISSN 2227-9709).

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 5495

Special Issue Editor

Prof. Dr. Dmitry Zinoviev

E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, Suffolk University, Boston, MA 02108, USA
Interests: social networks; network science; complexity; computer modeling and simulation; digital humanities

Special Issue Information

Dear Colleagues,

Complex network analysis (CNA), including social network analysis, has become a widespread data exploration, modeling, and analysis discipline. Complex networks can be used to describe natural, social, and artificial phenomena, such as food chains, rumor and infection spreading, text organization, and transportation networks. CNA applications solve the problems of identifying central objects and actors, information dissemination, controllability, and clustering via community detection.

CNA-based solutions emerge as a collaborative effort of scholars in social, behavioral, and life sciences, computer science, arts, and humanities. When applied to large (planetary-scale, “big data”) networks, such solutions often require novel approaches that ensure efficiency, scalability, and interpretability. 

We solicit original submissions that contribute novel network analysis methods and applications and use cases on any of the following topics:

  • Application of CNA in digital humanities, especially text analysis, digital history, and digital archaeology;
  • Adaptation of time-consuming complex network algorithms, such as community detection, to large networks;
  • Interpretation of complex network measures, such as centralities and clustering coefficients, in the context of non-flow-based complex networks (e.g., semantic networks);
  • Integration of traditional CNA methods with modern machine learning tools and algorithms (such as naive Bayesian classification, random forests, and neural networks).

Prof. Dr. Dmitry Zinoviev
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Informatics is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • complex networks
  • social network analysis
  • digital humanities
  • centralities
  • semantic networks
  • digital history
  • visualization
  • computational social science
  • community detection

Published Papers (2 papers)

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17 pages, 762 KiB  
Article
Modeling the Influence of Fake Accounts on User Behavior and Information Diffusion in Online Social Networks
by Sara G. Fahmy, Khaled M. Abdelgaber, Omar H. Karam and Doaa S. Elzanfaly
Informatics 2023, 10(1), 27; https://doi.org/10.3390/informatics10010027 - 03 Mar 2023
Cited by 2 | Viewed by 2714
Abstract
The mechanisms of information diffusion in Online Social Networks (OSNs) have been studied extensively from various perspectives with some focus on identifying and modeling the role of heterogeneous nodes. However, none of these studies have considered the influence of fake accounts on human [...] Read more.
The mechanisms of information diffusion in Online Social Networks (OSNs) have been studied extensively from various perspectives with some focus on identifying and modeling the role of heterogeneous nodes. However, none of these studies have considered the influence of fake accounts on human accounts and how this will affect the rumor diffusion process. This paper aims to present a new information diffusion model that characterizes the role of bots in the rumor diffusion process in OSNs. The proposed SIhIbR model extends the classical SIR model by introducing two types of infected users with different infection rates: the users who are infected by human (Ih) accounts with a normal infection rate and the users who are infected by bot accounts (Ib) with a different diffusion rate that reflects the intent and steadiness of this type of account to spread the rumors. The influence of fake accounts on human accounts diffusion rate has been measured using the social impact theory, as it better reflects the deliberate behavior of bot accounts to spread a rumor to a large portion of the network by considering both the strength and the bias of the source node. The experiment results show that the accuracy of the SIhIbR model outperforms the SIR model when simulating the rumor diffusion process in the existence of fake accounts. It has been concluded that fake accounts accelerate the rumor diffusion process as they impact many people in a short time. Full article
(This article belongs to the Special Issue Applications of Complex Networks: Advances and Challenges)
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22 pages, 796 KiB  
Article
Discovering Entities Similarities in Biological Networks Using a Hybrid Immune Algorithm
by Rocco A. Scollo, Antonio G. Spampinato, Georgia Fargetta, Vincenzo Cutello and Mario Pavone
Informatics 2023, 10(1), 18; https://doi.org/10.3390/informatics10010018 - 31 Jan 2023
Viewed by 1706
Abstract
Disease phenotypes are generally caused by the failure of gene modules which often have similar biological roles. Through the study of biological networks, it is possible to identify the intrinsic structure of molecular interactions in order to identify the so-called “disease modules [...] Read more.
Disease phenotypes are generally caused by the failure of gene modules which often have similar biological roles. Through the study of biological networks, it is possible to identify the intrinsic structure of molecular interactions in order to identify the so-called “disease modules”. Community detection is an interesting and valuable approach to discovering the structure of the community in a complex network, revealing the internal organization of the nodes, and has become a leading research topic in the analysis of complex networks. This work investigates the link between biological modules and network communities in test-case biological networks that are commonly used as a reference point and which include Protein–Protein Interaction Networks, Metabolic Networks and Transcriptional Regulation Networks. In order to identify small and structurally well-defined communities in the biological context, a hybrid immune metaheuristic algorithm Hybrid-IA is proposed and compared with several metaheuristics, hyper-heuristics, and the well-known greedy algorithm Louvain, with respect to modularity maximization. Considering the limitation of modularity optimization, which can fail to identify smaller communities, the reliability of Hybrid-IA was also analyzed with respect to three well-known sensitivity analysis measures (NMI, ARI and NVI) that assess how similar the detected communities are to real ones. By inspecting all outcomes and the performed comparisons, we will see that on one hand Hybrid-IA finds slightly lower modularity values than Louvain, but outperforms all other metaheuristics, while on the other hand, it can detect communities more similar to the real ones when compared to those detected by Louvain. Full article
(This article belongs to the Special Issue Applications of Complex Networks: Advances and Challenges)
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